Development of a Template Model and Simulation Approach for Quantifying the Effect of WLTP Introduction on Light Duty Vehicle CO2 Emissions and Fuel Consumption

Author(s):  
Dimitris Tsokolis ◽  
Stefanos Tsiakmakis ◽  
Georgios Triantafyllopoulos ◽  
Anastasios Kontses ◽  
Zisis Toumasatos ◽  
...  
Author(s):  
Saeed Vasebi ◽  
Yeganeh M. Hayeri ◽  
Constantine Samaras ◽  
Chris Hendrickson

Gasoline is the main source of energy used for surface transportation in the United States. Reducing fuel consumption in light-duty vehicles can significantly reduce the transportation sector’s impact on the environment. Implementation of emerging automated technologies in vehicles could result in fuel savings. This study examines the effect of automated vehicle systems on fuel consumption using stochastic modeling. Automated vehicle systems examined in this study include warning systems such as blind spot warning, control systems such as lane keeping assistance, and information systems such as dynamic route guidance. We have estimated fuel savings associated with reduction of accident and non-accident-related congestion, aerodynamic force reduction, operation load, and traffic rebound. Results of this study show that automated technologies could reduce light-duty vehicle fuel consumption in the U.S. by 6% to 23%. This reduction could save $60 to $266 annually for the owners of vehicles equipped with automated technologies. Also, adoption of automated vehicles could benefit all road users (i.e., conventional vehicle drivers) up to $35 per vehicle annually (up to $6.2 billion per year).


2016 ◽  
Vol 14 ◽  
pp. 3169-3178 ◽  
Author(s):  
A. Dimaratos ◽  
D. Tsokolis ◽  
G. Fontaras ◽  
S. Tsiakmakis ◽  
B. Ciuffo ◽  
...  

2019 ◽  
Vol 11 (11) ◽  
pp. 168781401988625 ◽  
Author(s):  
Lijun Hao ◽  
Chunjie Wang ◽  
Hang Yin ◽  
Chunxiao Hao ◽  
Haohao Wang ◽  
...  

In order to estimate the light-duty vehicle fuel economy at high-altitude areas, the coast-down tests of a passenger car on level road were conducted at different elevations, and the coast-down resistance coefficients were calculated. Furthermore, a fuel economy model for a light-duty vehicle adopting backward simulation method was developed, and it mainly consists of vehicle dynamic model, internal combustion engine model, transmission model, and differential model. The internal combustion engine model consists of the brake-specific fuel consumption maps as functions of engine torque and engine speed, and the brake-specific fuel consumption map near sea level was constructed based on engine experimental data, and the brake-specific fuel consumption maps at high altitudes were calculated by GT-Power Modeling of the internal combustion engine. The fuel consumption rate was calculated from the brake-specific fuel consumption maps and brake power and used to calculate the fuel economy of the light-duty vehicle. The model predicted fuel consumption data met well with the test results, and the model prediction errors are within 5%.


Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7915
Author(s):  
Isabella Yunfei Zeng ◽  
Shiqi Tan ◽  
Jianliang Xiong ◽  
Xuesong Ding ◽  
Yawen Li ◽  
...  

Private vehicle travel is the most basic mode of transportation, so that an effective way to control the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic growth as well as achieving a green low-carbon society. Therefore, the factors impacting individual carbon emissions must be elucidated. This study builds five different models to estimate the real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the light gradient boosting machine (LightGBM) model performs better than the linear regression, naïve Bayes regression, neural network regression, and decision tree regression models, with a mean absolute error of 0.911 L/100 km, a mean absolute percentage error of 10.4%, a mean square error of 1.536, and an R-squared (R2) value of 0.642. This study also assesses a large pool of potential factors affecting real-world fuel consumption, from which the three most important factors are extracted, namely, reference fuel-consumption-rate value, engine power, and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with the greatest impact are the vehicle brand, engine power, and engine displacement. The average air pressure, average temperature, and sunshine time are the three most important climate factors.


2021 ◽  
Vol 268 ◽  
pp. 01029
Author(s):  
Meng Zhou ◽  
Chongzhi Zhong ◽  
Jingyuan Li

Through the fuel consumption test of several listed vehicles in China, the basic fuel consumption results of cold start under CLTC-P cycle, the fuel consumption results of vehicles under the condition of air conditioning on, and the fuel consumption results of vehicles under the condition of air conditioning off are measured. At the same time, the differences between NEDC cycle and CLTC-P cycle in China's fuel consumption certification test are compared, and the results of fuel consumption test are combined The fuel consumption test results under CLTC-P cycle are higher than those under NEDC cycle, and the fuel consumption test procedures under Chinese condition are more in line with the actual driving situation in China.


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